User authentication is a decision problem where a test input is accepted or rejected based on its similarity to user's training inputs. The similarity is often computed in an embedding space, i.e., if the predicted embedding of the test input is close to the reference embedding, the input will be accepted, and otherwise rejected.
Authentication models need to be trained with a large variety of users' data so that the model learns different data characteristics and can reliably reject imposters.
However, due to the privacy-sensitivity of both the raw inputs and the user embeddings, it is not possible to centrally collect users' data to train the model.
Protecting data privacy is particularly important in UA applications, since the model is likely to be trained and tested in adversarial settings.
Specifically, leakage of embedding makes the authentication model vulnerable to both training- and inference-time attacks, e.g., poisoning [1]} and evasion attacks [2]}, [3]}.
